Generative Pretrained Transformers for Emotion Detection in a Code-Switching Setting
Andrew Nedilko · 2023
This paper describes the approach that we utilized to participate in the shared task for multi-label and multi-class emotion classification organized as part of WASSA 2023 at ACL 2023.The objective was to build models that can predict 11 classes of emotions, or the lack thereof (neutral class) based on codemixed Roman Urdu and English SMS text messages.We participated in Track 2 of this task -multi-class emotion classification (MCEC).We used generative pretrained transformers, namely ChatGPT because it has a commercially available full-scale API, for the emotion detection task by leveraging the prompt engineering and zero-shot / few-shot learning methodologies based on multiple experiments on the dev set.Although this was the first time we used a GPT model for the purpose, this approach allowed us to beat our own baseline character-based XGBClassifier, as well as the baseline model trained by the organizers (bertbase-multilingual-cased).We ranked 4th and achieved the macro F1 score of 0.7038 and the accuracy of 0.7313 on the blind test set.